Artificial Intelligence-Enabled Model for Early Detection of Left Ventricular Hypertrophy and Mortality Prediction in Young to Middle-Aged Adults.

Background: Concealed left ventricular hypertrophy (LVH) is a prevalent condition that is correlated with a substantial risk of cardiovascular events and mortality, especially in young to middle-aged adults. Early identification of LVH is warranted. In this work, we aimed to develop an artificial intelligence (AI)-enabled model for early detection and risk stratification of LVH using 12-lead ECGs.

Prenatal exposure to air pollutants and childhood atopic dermatitis and allergic rhinitis adopting machine learning approaches: 14-year follow-up birth cohort study.

The incidence of childhood atopic dermatitis (AD) and allergic rhinitis (AR) is increasing. This warrants development of measures to predict and prevent these conditions. We aimed to investigate the predictive ability of a spectrum of data mining methods to predict childhood AD and AR using longitudinal birth cohort data.